In recent years, state of the art brain imaging techniques like Functional Magnetic Resonance Imaging (fMRI), have raised new challenges to the statistical community, which is asked to provide new frameworks for modeling and data analysis. Here, motivated by resting state fMRI data, which can be seen as a collection of spatially dependent functional observations among brain regions, we propose a parsimonious but flexible representation of their dependence structure leveraging a Bayesian time-dependent latent factor model. Adopting an assumption of separability of the covariance structure in space and time, we are able to substantially reduce the computational cost and, at the same time, provide interpretable results. Theoretical properties of the model along with identifiability conditions are discussed. For model fitting, we propose a mcmc algorithm to enable posterior inference. We illustrate our work through an application to a dataset coming from the enkirs project, discussing the estimated covariance structure and also performing model selection along with network analysis. Our modeling is preliminary but offers ideas for developing fully Bayesian fMRI models, incorporating a plausible space and time dependence structure.

Caponera, A., Denti, F., Rigon, T., Sottosanti, A., Gelfand, A. (2018). Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data. In A. Canale (a cura di), Studies in Neural Data Science (pp. 111-130). Springer [10.1007/978-3-030-00039-4_7].

Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data

Denti, F;Rigon, T;
2018

Abstract

In recent years, state of the art brain imaging techniques like Functional Magnetic Resonance Imaging (fMRI), have raised new challenges to the statistical community, which is asked to provide new frameworks for modeling and data analysis. Here, motivated by resting state fMRI data, which can be seen as a collection of spatially dependent functional observations among brain regions, we propose a parsimonious but flexible representation of their dependence structure leveraging a Bayesian time-dependent latent factor model. Adopting an assumption of separability of the covariance structure in space and time, we are able to substantially reduce the computational cost and, at the same time, provide interpretable results. Theoretical properties of the model along with identifiability conditions are discussed. For model fitting, we propose a mcmc algorithm to enable posterior inference. We illustrate our work through an application to a dataset coming from the enkirs project, discussing the estimated covariance structure and also performing model selection along with network analysis. Our modeling is preliminary but offers ideas for developing fully Bayesian fMRI models, incorporating a plausible space and time dependence structure.
Capitolo o saggio
Bayesian factor analysis; Gaussian processes; Low-rank factorizations; Separable models;
English
Studies in Neural Data Science
978-3-030-00038-7
Caponera, A., Denti, F., Rigon, T., Sottosanti, A., Gelfand, A. (2018). Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data. In A. Canale (a cura di), Studies in Neural Data Science (pp. 111-130). Springer [10.1007/978-3-030-00039-4_7].
Caponera, A; Denti, F; Rigon, T; Sottosanti, A; Gelfand, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/215681
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